Furrr

DESCRIPTION

@@ -24,22 +24,17 @@ Imports:
   collapse,
   data.table,
   dodgr,
-  doSNOW,
   dplyr,
   digest,
-  foreach,
   future,
   furrr,
-  future.apply,
   geodist,
   httr,
   iotools,
   stringr,
   sf,
-  parallel,
   lubridate,
   purrr (>= 1.0),
-  pbapply,
   readr (>= 2.0),
   RcppSimdJson,
   tidyr,

R/atoc_export.R

@@ -433,7 +433,7 @@ makeCalendar <- function(schedule, ncores = 1) {
   CHECKROWS_NAME_VECTOR <- c(WEEKDAY_NAME_VECTOR, "duration", "start_date", "end_date")
 
 
-  res.calendar.days <- res.calendar[,..CHECKROWS_NAME_VECTOR]
+  res.calendar.days <- res.calendar[,CHECKROWS_NAME_VECTOR]
   res.calendar.days <- data.table::transpose(res.calendar.days)
   #transpose on the same size runs in around 3s, but causes named dataframe with mixed datatypes to be coerced to unnamed vector of integer.
 
@@ -444,7 +444,7 @@ makeCalendar <- function(schedule, ncores = 1) {
                              .f = checkrows,
                              .progress = TRUE)
     future::plan(future::sequential)
-    keep <- unlist(keep)
+
 
     # cl <- parallel::makeCluster(ncores)
     # parallel::clusterEvalQ(cl, {
@@ -458,6 +458,7 @@ makeCalendar <- function(schedule, ncores = 1) {
   } else {
     keep <- purrr::map(res.calendar.days, checkrows, .progress = TRUE)
   }
+  keep <- unlist(keep)
 
   res.calendar <- res.calendar[keep, ]
 
@@ -564,15 +565,22 @@ duplicate.stop_times_alt <- function(calendar, stop_times, ncores = 1) {
   }
 
   if (ncores == 1) {
-    stop_times.dup <- pbapply::pblapply(stop_times_split, duplicate.stop_times.int)
+    #stop_times.dup <- pbapply::pblapply(stop_times_split, duplicate.stop_times.int)
+    stop_times.dup <- purrr::map(stop_times_split, duplicate.stop_times.int, .progress = TRUE)
   } else {
-    cl <- parallel::makeCluster(ncores)
-    stop_times.dup <- pbapply::pblapply(stop_times_split,
-      duplicate.stop_times.int,
-      cl = cl
-    )
-    parallel::stopCluster(cl)
-    rm(cl)
+    # cl <- parallel::makeCluster(ncores)
+    # stop_times.dup <- pbapply::pblapply(stop_times_split,
+    #   duplicate.stop_times.int,
+    #   cl = cl
+    # )
+    # parallel::stopCluster(cl)
+    # rm(cl)
+
+    future::plan(future::multisession, workers = ncores)
+    res <- furrr::future_map(.x = stop_times_split,
+                             .f = duplicate.stop_times.int,
+                             .progress = TRUE)
+    future::plan(future::sequential)
   }
 
   stop_times.dup <- dplyr::bind_rows(stop_times.dup)

R/atoc_import.R

@@ -273,7 +273,7 @@ strip_whitespace <- function(df) {
 #'
 #' @param file Path to .mca file
 #' @param silent logical, should messages be displayed
-#' @param ncores number of cores to use when paralell processing
+#' @param ncores number of cores to use when parallel processing
 #' @param full_import import all data, default FALSE
 #' @param working_timetable use rail industry scheduling times instead of public times
 #' @export

R/atoc_nr.R

@@ -3,7 +3,7 @@
 #' Convert ATOC CIF files from Network Rail to GTFS
 #'
 #' @param path_in Character, path to Network Rail ATOC file e.g."C:/input/toc-full.CIF.gz"
-#' @param silent Logical, should progress messages be surpressed (default TRUE)
+#' @param silent Logical, should progress messages be suppressed (default TRUE)
 #' @param ncores Numeric, When parallel processing how many cores to use
 #'   (default 1)
 #' @param locations where to get tiploc locations (see details)

R/atoc_shapes.R

@@ -93,7 +93,8 @@ ATOC_shapes <- function(gtfs) {
     }
   }
 
-  dp.list <- pbapply::pblapply(dp.list, path_to_sf, verts = verts)
+  #dp.list <- pbapply::pblapply(dp.list, path_to_sf, verts = verts)
+  dp.list <- purrr::map(dp.list, path_to_sf, verts = verts, .progress = TRUE)
   dp.list <- unname(dp.list)
   pairs$geometry <- sf::st_sfc(dp.list, crs = 4326)
   rm(dp.list, verts)
@@ -110,7 +111,8 @@ ATOC_shapes <- function(gtfs) {
   }
 
   message(paste0(Sys.time()," Invert routes"))
-  pairs_opp$geometry <- pbapply::pblapply(pairs_opp$geometry, invert_linestring)
+  #pairs_opp$geometry <- pbapply::pblapply(pairs_opp$geometry, invert_linestring)
+  pairs_opp$geometry <- purrr::map(pairs_opp$geometry, invert_linestring, .progress = TRUE)
   pairs_opp$geometry <- sf::st_as_sfc(pairs_opp$geometry, crs = 4326)
   pairs_opp <- sf::st_as_sf(pairs_opp)
   pairs_opp <- pairs_opp[, names(pairs)]
@@ -135,7 +137,8 @@ ATOC_shapes <- function(gtfs) {
 
   message(paste0(Sys.time()," final formatting"))
   rm(graph, pairs)
-  shape_res <- pbapply::pblapply(st_split, match_lines)
+  #shape_res <- pbapply::pblapply(st_split, match_lines)
+  shape_res <- purrr::map(st_split, match_lines, .progress = TRUE)
 
   str5 <- lapply(shape_res, `[[`, 2)
   shapes <- lapply(shape_res, `[[`, 1)

R/extdata.R

@@ -130,7 +130,7 @@ check_data <- function(default_tag = "v0.1.2"){
 #'
 #' The ATOC data has inaccurate locations for many tiplocs, this is an improved dataset
 #'
-#' "naptan_missing" Bus Stop Locations missing from NapTAN
+#' "naptan_missing" Bus Stop Locations missing from NaPTAN
 #'
 #' A database of bus stops that are missing from the NAPTAN but are known to
 #' have been used. For some reason the official NAPTAN file is missing a small

R/get_cal.R

@@ -7,7 +7,7 @@
 #' @return data frame
 #' @details TransXchange records bank holidays by name (e.g. Christmas Day),
 #'   some UK bank holidays move around, so this function downloads the official
-#'   bank holiday calendar. The offical feed only covers a short period of time
+#'   bank holiday calendar. The official feed only covers a short period of time
 #'   so this may not be suitable for converting files from the past / future.
 #' @export
 #'

R/get_naptan.R

@@ -6,7 +6,7 @@
 #' @param naptan_extra data frame of missing stops default uses `naptan_missing`
 #' @return data frame of stop locations
 #' @details TransXchange does not store the location of bus stops, so this
-#'   functions downloads them from the offical DfT source. NaPTAN has some
+#'   functions downloads them from the official DfT source. NaPTAN has some
 #'   missing bus stops which are added by UK2GTFS. See `naptan_missing`
 #'
 #'

R/globals.R

@@ -13,6 +13,13 @@ utils::globalVariables(c(
   'agency_lang','agency_id',   'Freq',
   'UID','hash','vehicle_type','running_board','service_number',
   'operator_code','route_id',
-  'speed_after','distance','school_terms','distance_after','historic_bank_holidays'
+  'speed_after','distance','school_terms','distance_after','historic_bank_holidays',
+  'runs_monday','runs_tuesday','runs_wednesday','runs_thursday','runs_friday',
+  'runs_saturday','runs_sunday', 'total_sunday',
+  'runs_Mon','runs_Tue','runs_Wed','runs_Thu','runs_Fri',
+  'runs_Sat','runs_Sun',
+  'tot_Mon','tot_Tue','tot_Wed','tot_Thu','tot_Fri',
+  'tot_Sat','tot_Sun',
+  'service_id','zone_id','time_bands','stop_name'
 ))
 

R/gtfs_cleaning.R

@@ -100,7 +100,7 @@ gtfs_fast_trips <- function(gtfs, maxspeed = 83, routes = TRUE) {
 }
 
 #' Find fast stops
-#' @description A varient of gtfs_fast_trips that can detect stops that may be in the wrong location
+#' @description A variant of gtfs_fast_trips that can detect stops that may be in the wrong location
 #' @param gtfs list of gtfs tables
 #' @param maxspeed the maximum allowed speed in metres per second default 83 m/s
 #'   (about 185 mph the max speed of trains on HS1 line)

R/gtfs_interpolate_times.R

@@ -36,16 +36,24 @@ gtfs_interpolate_times <- function(gtfs, ncores = 1){
   stop_times <- dplyr::group_split(stop_times)
 
   if(ncores == 1){
-    stop_times <- pbapply::pblapply(stop_times, stops_interpolate)
+    #stop_times <- pbapply::pblapply(stop_times, stops_interpolate)
+    stop_times <- purrr::map(stop_times, stops_interpolate, .progress = TRUE)
   } else {
-    cl <- parallel::makeCluster(ncores)
-    parallel::clusterEvalQ(cl, {loadNamespace("UK2GTFS")})
-    stop_times <- pbapply::pblapply(stop_times,
-                                    stops_interpolate,
-                                    cl = cl
-    )
-    parallel::stopCluster(cl)
-    rm(cl)
+    # cl <- parallel::makeCluster(ncores)
+    # parallel::clusterEvalQ(cl, {loadNamespace("UK2GTFS")})
+    # stop_times <- pbapply::pblapply(stop_times,
+    #                                 stops_interpolate,
+    #                                 cl = cl
+    # )
+    # parallel::stopCluster(cl)
+    # rm(cl)
+
+    future::plan(future::multisession, workers = ncores)
+    keep <- furrr::future_map(.x = stop_times,
+                              .f = stops_interpolate,
+                              .progress = TRUE)
+    future::plan(future::sequential)
+
   }
 
   stop_times <- data.table::rbindlist(stop_times)